ABSTRACT
Uncertainty in engineering analysis and design arises from several sources (Oberkampf et al, 1999). Some of the "known" sources are: (1) Physical uncertainty or inherent variability: The demands on an engineering system as well as its properties always have some variability associated with them, due to environmental factors and variations in operating conditions, manufacturing processes, quality control etc. Such quantities are represented in engineering analysis as random variables, with statistical parameters such as mean values, standard deviations, distribution types etc. estimated from observed data. (2) Informational Uncertainty: This includes several types of uncertainty associated with the type of information available: statistical uncertainty due to small number of samples, imprecise information, etc. The accuracy of the statistical distribution parameters depends on the amount of data available. Thus the distribution parameters themselves are uncertain, and have to be treated as random variables. On the other hand, information may be imprecise or qualitative, and it is not easy to treat this type of uncertainty through random variables. (3) Modeling Error: This results from approximate mathematical models of the system behavior, and from numerical approximations during the computational process. For new and complex engineering systems, this type of uncertainty is not quantifiable a priori.
